[pymvpa] 2fold balanced partitioner
basile.pinsard at gmail.com
Tue Jul 12 20:54:19 UTC 2016
It seems pretty simple but I cannot find a way to have a sensible 2fold
partitioner with balanced targets, feeding a balanced dataset (4 classes x
Optionally it would be sensible if all samples are used in testing the same
number of times.
Sifter([('partitions', 2),('targets', dict(balanced=True)) ]) ])
does generate balanced partitions, but will have variable number of cv
folds, which is a problem.
does balance the partitions by eliminating some sample but this reduces the
number of samples in training/testing sets, and not in a consistent way
FactorialPartitioner does the job but the count parameter is not working
(generate method is overloaded), then it's combinatorial yield thousands of
splits which is a bit much.
Would there be a way to repeatedly splits randomly taking half of each
classes samples in both of the partitions?
Or maybe should we make FactorialPartitioner to respect Partitioner
prototype (count/strategy parameters)?
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Pkg-ExpPsy-PyMVPA